Introduction to deep learning
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Due Apr 12, 2:59 AM EDT
What does the analogy “AI is the new electricity” refer to?
Yes. AI is transforming many fields from the car industry to agriculture to supply-chain...
Which of these are reasons for Deep Learning recently taking off? (Check the three options that apply.)
These were all examples discussed in lecture 3.
Yes! The development of hardware, perhaps especially GPU computing, has significantly improved deep learning algorithms' performance.
Yes! The digitalization of our society has played a huge role in this.
Recall this diagram of iterating over different ML ideas. Which of the statements below are true? (Check all that apply.)
Yes, as discussed in Lecture 4.
Yes, as discussed in Lecture 4.
Yes. For example, we discussed how switching from sigmoid to ReLU activation functions allows faster training.
When an experienced deep learning engineer works on a new problem, they can usually use insight from previous problems to train a good model on the first try, without needing to iterate multiple times through different models. True/False?
Yes. Finding the characteristics of a model is key to have good performance. Although experience can help, it requires multiple iterations to build a good model.
Which one of these plots represents a ReLU activation function?
Correct! This is the ReLU activation function, the most used in neural networks.
Images for cat recognition is an example of “structured” data, because it is represented as a structured array in a computer. True/False?
Yes. Images for cat recognition is an example of “unstructured” data.
A demographic dataset with statistics on different cities' population, GDP per capita, economic growth is an example of “unstructured” data because it contains data coming from different sources. True/False?
A demographic dataset with statistics on different cities' population, GDP per capita, economic growth is an example of “structured” data by opposition to image, audio or text datasets.
Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? (Check all that apply.)
Yes. We can train it on many pairs of sentences x (English) and y (French).
Yes. An RNN can map from a sequence of english words to a sequence of french words.
In this diagram which we hand-drew in lecture, what do the horizontal axis (x-axis) and vertical axis (y-axis) represent?
Assuming the trends described in the previous question's figure are accurate (and hoping you got the axis labels right), which of the following are true? (Check all that apply.)
Yes. Bringing more data to a model is almost always beneficial.
Yes. According to the trends in the figure above, big networks usually perform better than small networks.